Friday, November 2, 2012

There Will Be Graphs: Part Six of a Series

By Evan D. Robertson, Project Associate.

If you’ve ever used LinkedIn, or Facebook for that matter, you’ve
probably noticed a pesky little box somewhere in the upper right hand
corner with a head line that reads something to the effect of “people
you may know.” Often times, these connections are dead on. Other times,
you’ll get a random stranger, or worse, people with whom you just aren’t
on speaking terms. Awkwardness aside, both social networking websites
use a generally straightforward process to determine whom to recommend.
It’s called “triangle closing.” Triangle closing can help you identify
potential connection opportunities, allowing you to improve the overall
connectivity of your network. Information having trouble reaching some
of your Chamber members? Increasing the connections between your members
might improve the situation. But, I am getting a little ahead of
myself. First, I’d like to go through the highly self-conscious process
of analyzing my own social network. For this analysis, I simply
downloaded my Facebook friends network, and looked at the relationships
between my friends in the context of my whole Facebook network.
While analyzing your own social network will likely have you
narcissistically asking whether this is really all the people you know,
it is equally interesting, and if you’re a business professional,
insightful. My social network, containing all 173 of my friends on
Facebook, is pictured above. Nowhere close to the few thousand that most
people seemingly “know” on the web these days, but I’ve always been a
quality over quantity type of guy.

Overall, my social network is fairly disconnected. My Facebook density
is .087, with a value of 1.00 equaling a network in which everyone is
connected. In other words, my network has about 8.7 percent of the total
possible connections. Interestingly the average degree of my network
(the average number of people an individual is connected to) is 14.89,
so the average person in my network knows about 15 of my other Facebook
friends. Both the lack of connectivity in my network and the number of
interconnections isn’t surprising as my social network is largely a time
lapse.
Three large clusters appear in my friends network, they are highly
correlated with time (no regression needed, I lived it). The three
clusters are centered around three important periods in my life: high
school, my undergraduate education at Georgia State, and graduate school
at Georgia Tech. Each cluster is loosely connected to one another, with
only a few of my friends serving as network bridges. As an aside, it
was remarkable to see who these network bridges were as they weren’t who
I was expecting. Now, for the important bit: why does this matter?

Let’s, hypothetically, say that my Georgia Tech friends are throwing me a
birthday party. They want to get as many of my close friends as
possible to the party. Come party day, I arrive, and I am surprised.
Surprised to notice that everyone I know from Georgia Tech is there, and
a few people I know from high school, but absolutely no one I know from
my soul-searching period at Georgia State is in attendance. Is my
surprise valid? Not at all. Here’s why.
The above graph displays the shortest path (in red) between my most
connected Georgia Tech (Point A) friend and a Georgia State friend
selected at random (Point B). In order for my Georgia State friend to
receive an invitation to my surprise party, my Georgia Tech friend would
have to communicate with four other people. More accurately, the
information would have to flow directly through two specific people
(network bridges) in order to have the remote possibility of reaching my
Georgia State network. Even if the information was able to find these
bridges, it still has to deal with the possibility of information being
muddled. It’s similar to the game of telephone you played when you were a
child: some information is bound to be inaccurate by the time it gets
to its final recipient. In this case, maybe my Georgia State friends get
directions to the wrong house. With this knowledge, is there any way to
ensure that my Georgia State network is more represented in my next
birthday extravaganza?

Quite simply, yes. It is the exact same method that LinkedIn uses to
identify “people you may know.” How does it work? It’s easily
demonstrated pictorially.
Above is my graduate school network with three people highlighted in
red. This is a partial triangle, one person knows two other people,
however, those two other people don’t know one another. Triangle closing
would entail connecting these two other people as shown by the green
line below.
With the triangle closed, my network has become denser; information is
able to flow more freely than before. If I complete this process a
number of times, I can begin to establish more network bridges between
the three distinct clusters of my social network. Information, thus, can
flow more freely.

Understanding your social network, whether it’s your own or your
organization’s, can give you greater insight into how you are connecting
with your target audience. Imagine if my network were members of a
local Chamber of Commerce, understanding that the network is clustered
into three distinct groups would help me to understand what types of
connections I should begin to make and who would best be able to bridge
relationships across these distinct groups. With the explosion of social
networking tools such as LinkedIn, Twitter, and Facebook, having
greater control over your communication strategy and understanding your
audience (and connections between your audience members) can improve the
efficacy of your social media campaigns, especially in ascertaining why
certain information isn’t getting to some of your audience. If nothing
else, it can create some really cool graphs.